Diffusion MRI (dMRI) suffers from heavy noise, which undermines the accuracy and reliability of the subsequent quantitative analysis. Traditional deep learning denoising methods typically depend on training with paired noisy and clean data, which are unavailable in practice. Self-supervised techniques, such as DDM2, overcomes this limitation with the diffusion model. However, DDM2 is plagued by high computational cost and unsatisfactory performance when dealing with heavy noise. To tackle these challenges, we propose a novel self-supervised dMRI denoising model, called Efficient Collaborative Diffusion Model (ECDM). Specifically, we first employ a Noise2Noise-like method to obtain coarse denoised dMRI data. Subsequently, we use a latent encoder to compress the coarse data into a highly compact latent space. A diffusion model is then trained within this latent space to generate prior features. These features are passed to the denoising network through a hierarchical architecture and a cross-attention component for collaborative fine noise reduction. Our method not only achieves effective noise reduction with a collaborative coarse-to-fine framework but also enhances the efficiency of the diffusion model by utilizing the compact latent representation. Extensive experiments on both simulated and real datasets demonstrate that ECDM surpasses existing dMRI denoising methods remarkably.